Effects of confounding and effect-modifying lifestyle, environmental and medical factors on risk of radiation-associated cardiovascular disease

Background Cardiovascular disease (CVD) is the leading cause of death worldwide. It has been known for some considerable time that radiation is associated with excess risk of CVD. A recent systematic review of radiation and CVD highlighted substantial inter-study heterogeneity in effect, possibly a result of confounding or modifications of radiation effect by non-radiation factors, in particular by the major lifestyle/environmental/medical risk factors and latent period. Methods We assessed effects of confounding by lifestyle/environmental/medical risk factors on radiation-associated CVD and investigated evidence for modifying effects of these variables on CVD radiation dose–response, using data assembled for a recent systematic review. Results There are 43 epidemiologic studies which are informative on effects of adjustment for confounding or risk modifying factors on radiation-associated CVD. Of these 22 were studies of groups exposed to substantial doses of medical radiation for therapy or diagnosis. The remaining 21 studies were of groups exposed at much lower levels of dose and/or dose rate. Only four studies suggest substantial effects of adjustment for lifestyle/environmental/medical risk factors on radiation risk of CVD; however, there were also substantial uncertainties in the estimates in all of these studies. There are fewer suggestions of effects that modify the radiation dose response; only two studies, both at lower levels of dose, report the most serious level of modifying effect. Conclusions There are still large uncertainties about confounding factors or lifestyle/environmental/medical variables that may influence radiation-associated CVD, although indications are that there are not many studies in which there are substantial confounding effects of these risk factors.


Introduction
Cardiovascular disease (CVD) is the leading cause of death worldwide [1][2][3].The main independent risk factors for CVD are cigarette smoking, hypertension, diabetes, obesity and elevated total cholesterol or elevated low density lipoprotein (LDL) cholesterol [4][5][6].It has been known for some considerable time that high dose radiotherapy is also associated with excess risk of CVD [7,8].More recently, it has become clear that there are also radiation-associated excess risks in the Life Span Study (LSS) of the Japanese atomic-bomb survivors [9,10], and in a number of groups exposed at still lower levels of radiation dose and at lower dose rates [11].A recent systematic review and meta-analysis of epidemiological studies highlighted evidence of association between radiation exposure and CVD at high dose, and to a lesser extent at low dose, with some indications of differences in risk between acute and chronic exposures [12].There was inter-study heterogeneity, possibly a result of confounding or modifications of radiation effect by other factors, which complicates a causal interpretation of these findings [12].Although a number of studies assessed in the previous review adjusted for many of the major lifestyle risk factors, relatively few studies undertook investigation of the modifying effect of these risk factors on the radiation associated CVD.
In this paper we assess effects of confounding by lifestyle, environmental, and medical risk factors, and also investigate evidence for modifying effects of these variables on radiation dose response.

Methods
The data used are those assembled in a recent systematic review [12].In brief, the review was conducted, and reported according to PRISMA and registered in PROSPERO (https:// www.crd.york.ac.uk/ prosp ero/) (reg.no.202036).PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection were used to systematically search the literature, with no limits applied (date, language), on 6th October 2022.We excluded animal studies, and any study without an abstract.The database search yielded a total of 15,098 articles.

Main exposure
Only those studies with individual organ dosimetry that enable estimation of excess relative risk per unit absorbed dose in Gy (ERR/Gy) in relation to heart or brain dose or other closely related tissue doses were used.

Potentially confounding and effect modifying variables considered
We only used those studies in which there was adjustment for any factor other than the standard demographic risk factors (age, sex, year of birth etc.), and in which ERR/Gy were reported both with and without adjustment, or alternatively in which ERR/Gy were reported of the modifying effect on radiation response of these variables.In other words, to assess potential confounding a relative risk model had been fitted of the form RR = exp[βV ](1 + αD) with adjustment for a potentially modifying variable V and also without adjustment for that potentially modifying variable V .A modifying variable in a particular study was any variable V for which had been assessed the interactions with radiation dose, in other words in which a model had been fitted of the form RR = 1 + αD exp [βV ] .In some cases the only reported effect was a p-value (e.g. of significance of modification).All these studies are listed in Tables 1 and 2.

Methods to evaluate the effects of confounding and effect modifying variables
The methods employed to assess the effects of potential confounding variable are comparison of the fitted ERR unadjusted for the potential confounding variable, ERR unadj , and the ERR ERR adj [V ] adjusted for the confounding variable V .We categorized those estimates in which adjustment for potential confounders resulted in changes of the following magnitudes: a) more than 50% difference, i.e., with ratio of estimates outside the interval [0.667, 1.5] -labelled * b) more than 100% difference, i.e., with ratio of estimates outside the interval [0.5, 2.0] -labelled ** c) estimates with different signs, i.e. one positive, the other negative, labelled *** Likewise any variable whose interaction with radiation dose resulted in the following degree of change in the ERR was labelled as follows: a) more than 50% difference, i.e., with ratio of estimates (with/without modification) outside the interval   *** ratio of adjusted to unadjusted estimates negative † ratio of estimate with interaction modifier to that without modifier outside the interval [0.667, 1.5], † † ratio of estimate with interaction modifier to that without modifier outside the interval [0.5, 2] † † † ratio of estimate with interaction modifier to that without modifier negative a estimate derived by fitting a linear model by (inverse-variance) weighted least squares, applied to the aggregate data provided in Table 3 of Mulrooney et al. [13] and in Table 2 of Shrestha et al. [36].For the data of Mulrooney et al. [13] (all endpoints except all cardiac disease) average cardiac doses of 0, 7.5, 25, and 45 Gy were assumed for the respective groups with the following specified ranges of cardiac doses: 0, 1-15, 15.1-34.99Gy, ≥ 35 Gy.For the data of Shrestha et al. [36] average cardiac doses of 0, 5, 15, 25 and 35 Gy were assumed for the respective groups with the following specified ranges of cardiac doses: 0, 0.1-9.9,10-19.9,20-29.9Gy, ≥ 30 Gy, and the central estimates of ERR/Gy given in Figure 5 of Shrestha et al. [36]           [0.667, 1.5] and the heterogeneity was statistically significant (p < 0.05) -labelled † b) more than 100% difference, i.e., with ratio of estimates (with/without modification) outside the interval [0.5, 2.0] and the heterogeneity was statistically significant (p < 0.05) -labelled † † c) estimates with different signs, i.e. one (with modification) positive, the other (without modification) negative or vice versa and the heterogeneity was statistically significant (p < 0.05), labelled † † † We specifically highlight the most serious discrepancies of both sorts (***, † † †) in the text below.We describe these as potential confounders and potential effect modifiers, respectively.Table 3 reports those studies and results (taken from Tables 1, 2) in which one of these six categories of potentially confounding or modifying effects is observed.Table 4 separately reports effects of variation of latency.

Results
Of the total of 93 studies from the original systematic review and meta-analysis (detailed in Little et al. [12]), 43 studies satisfied the a priori selection criteria and were retained for this analysis.
Of the 50 studies that were omitted, the high dose radiotherapeutic studies (of the type shown in Table 1) in many cases  had information on many lifestyle and environmental variables, but only presented one type of analysis (generally fully adjusted); however, there were some high dose studies in which there was little or no information on potential confounders [80,81].The Danish study of Lorenzen et al. [82] was omitted as it is largely subsumed by the Nordic study of Darby et al. [19].There was a similar situation for the lower dose studies (of the type shown in Table 2), which in some cases had rich lifestyle information but only presented a single type of analysis [83][84][85][86][87][88] although in many instances the lower dose studies that were omitted had little or no information, apart from crude markers of socioeconomic status [54,.Of the final selected studies, 22 were of groups exposed to substantial doses of medical radiation for therapy or diagnosis (Table 1).The remaining 21 studies were of groups exposed at much lower levels of dose and/or dose rate (Tables 2 and 3).There is substantial overlap in the populations studied in some of these groups.For example three of the studies relate to various CVD endpoints in the LSS [9,10,37], and there are various studies of a number of CVD endpoints in the Mayak nuclear workers [40][41][42][43][44][45][46].

Modifying effects of radiation
There are only two studies reporting the most serious level of modifying effect, those of the International Nuclear Workers Study (INWORKS) workers by Gillies et al. [39] and the Korean medical diagnostic worker study of Cha et al. [51] (Tables 2 and 3).Gillies et al. [39] reported markedly higher risks for females, with ERR/Gy of 4.22 (90% CI 1.72 to 7.21) for all CVD, 6.17 (90% CI 2.44 to 10.92) for ischemic heart disease (IHD), and 2.67 (90% CI < 0 to 9.79) for CeVD, compared with ERR/Gy for males for the same endpoints of 0.20 (90% CI 0.07 to 0.36), 0.16 (90% CI -0.01 to 0.34), and 0.48 (90% CI 0.10 to 0.91), respectively; these differences were highly significant for all CVD (p = 0.005) and IHD (p = 0.004), but not for CeVD (p > 0.50) (Tables 2 and 3).Gillies et al. [39] also reported modifications by attained age and duration of employment, and although some of these were substantial for certain groups (Tables 2 and 3) none were statistically significant (p > 0.10).The Korean worker study of Cha et al. [51] also reported markedly higher ERR/Gy for females, with CVD ERR/Gy of 42.1 (95% CI 3.0 to 99.2) compared with ERR/Gy for males of -0.7 (95% CI -7.6 to 7.6).This study also reported     (10) personal dose equivalent at 10 mm depth, LDL low density lipoprotein, OR odds ratio, SES socioeconomic status * ratio of adjusted to unadjusted estimates outside the interval [0.667, ** ratio of adjusted to unadjusted estimates outside the interval [0.5, 2] *** ratio of adjusted to unadjusted estimates negative † ratio of estimate with interaction modifier to that without modifier outside the interval [0.667, 1.5] † † ratio of estimate with interaction modifier to that without modifier outside the interval [0.5, 2] † † † ratio of estimate with interaction modifier to that without modifier negative a analysis derived from Table 3 of Yamada et al.  2 of Markabayeva et al. [53].Median cardiac doses of 0.009, 0.041, 0.070, and 0.326 Sv were assumed for the respective groups with the following specified ranges of effective doses: < 20, 20-59, 60-185, > 185 mSv, as given by Markabayeva et al. [53] significant modifications of ERR/Gy for CVD in relation to age, birth year, year started work and years worked (Tables 2 and 3).There were similar indications of heterogeneity for all these variables for hypertension and CVD excluding CeVD and other CVD (ICD10 I70-I83, I85-99).The French Childhood Cancer Study of Mansouri et al. [16] reported marked discrepancies with treatment status by anthracyclines (cardiotoxic anticancer drugs), with an ERR/Gy of 0.44 (95% CI 0.18 to 1.12) for those not treated compared with an ERR/Gy of 0.09 (95% CI 0.02 to 0.22) for those treated with anthracyclines (Table 1 1).In no other high radiation dose studies (reported in Table 1) were any modifying effects reported [17-19, 22, 23].In the LSS, there are significant modifying effects on CeVD risk of attained age and age at exposure [10,11], with ERR decreasing in each case with increases in each variable, but no such modifications in ERR by any of these variables when the investigators used only CVD as outcome; Little et al. [11] reported a change in ERR/Gy per year of age at exposure by -0.050 (95% CI -0.099, -0.015) for stroke and by -0.012 (95% CI -0.041, 0.018) for heart disease.There is a borderline significant effect (p = 0.022) of sex on heart disease but not for stroke (p > 0.9) [11].
Although not reported in Table 2, because the analysis was not generally aligned with that of the main analysis (using a lag of 5 years, and wherever possible dose < 4 Gy), there is analysis of the Mayak worker data by sex, attained age and duration of employment group; there was no statistically significant heterogeneity (p > 0.1) in effect suggested by these analyses [41,42,[44][45][46].

Modifying effects of latency
Table 4 shows the modifying effects of latency.There is very little variation of ERR/Gy with lag, although there is a slight tendency for ERR/Gy to increase with increasing lag period.

Modifying effects in animal data
Table 5 illustrates what little is known about potential modifying factors from radiobiological animal data.There is some evidence of the modifying effects of age at exposure and chemotherapy in certain systems.

Discussion
We have assessed effects of confounding by lifestyle, environmental or medical risk factors on radiation-associated CVD, using data assembled for a recent systematic review [12].We found only limited evidence that adjustment for potential confounding made substantial difference to risk estimates.Only in four studies, in a group treated in childhood for hemangioma [34], in the LSS [9] and in two groups of nuclear workers [48,51], were the adjusted and unadjusted ERR substantially different (Tables 1, 2 and 3).However, it was hard to assess whether these variables were true confounders of the association between radiation and CVD; there were also substantial uncertainties in all of these studies, so that not much weight can be attached.
We also investigated evidence for modifying effects of these variables on CVD radiation dose response, again using data assembled for the systematic review [12].There are fewer suggestions of the most serious level of modifying effect, with age at exposure modifications in the same direction reported in two studies [11,16], although for different disease endpoints.In the study of Mansouri et al. [16] there were substantial modifying effects of anthracycline exposure (Tables 1, 2 and 3).In the LSS and in two groups of nuclear workers there are significant modifying effects of sex [11,39,51], although for discrepant endpoints.However, in most of the studies reported here no analysis has been reported of modifying effects of these or any other variable (Tables 1, 2 and 3).There is little variation of ERR with lagging period, although there is a slight tendency for ERR/Gy to increase with increasing lag period.(Table 4).
The radiobiological animal data has rather less information (Table 5).The metrics used are heterogeneous, and in general the internal dose trends (ERR/Gy) used in the epidemiological data given in Tables 1, 2 and 3 are not given.Indeed, in most studies there is only a single irradiated group, and the relative effects of the extra covariate on the radiation-associated relative risk (irradiated vs control) difficult to determine.Given the heterogeneity in endpoints used and in the animal systems employed one should probably not attach much weight to these findings.
Assessment of outcomes is a complication, as mortality outcomes could be less accurate than studies of incidence.In incidence studies, medical and lifestyle factors are more likely to be collected, as reported in our systematic review [12].Pooling mortality and incidence data could explain part of the heterogeneity of the summarized results observed in the meta-analysis.Indeed, summarized risks were significantly higher for mortality endpoints compared with those of incidence in the meta-analysis [12].An additional complication in       [38,39], Mayak nuclear workers [40][41][42][44][45][46]) is the overlap in subjects included, a feature also of the systematic review from which they were drawn [12].It is very likely that there is inter-study heterogeneity of effect in the present study, reflecting the heterogeneity that was observed in the systematic review from which it is drawn [12].Some part of the heterogeneity in the previous review is clearly driven by differences in endpoint sensitivity, by age at exposure, by dose and dose rate, and as noted above mortality vs incidence, but even after accounting for the effect of these heterogeneity remained [12].Such heterogeneity complicates any causal interpretation of the results presented.
Confounding is likely to be specific to each study and the effects of adjustment could rarely be generalized to other studies.Residual confounding could be an issue, if the potential confounding variable is measured with substantial error.Effect modification is likely to be much more easily compared between studies, although the evidence assembled here does not suggest that even for such easily and reliably measured variables as sex and age at exposure there are consistent effects within studies (Tables 1, 2 and 3).These differences in modifying effect between cohort may reflect the play of chance, but it is also possible that there are underlying differences between the cohorts.Medical and lifestyle factors are differently available in the studies considered, with more detailed information among studies of radiotherapeutic exposure, where radiation doses are high to moderate.However, the number of patients included in these medical studies is generally rather small, limiting study of specific endpoints.In contrast, in studies on workers or in general population, generally few potential confounding variables can be collected as the large number of people recruited and the way of collecting the information does not usually allow such information to be obtained.In our systematic review [12], among the lower dose studies with detailed information on lifestyle factors and a large number of included people there were only a few occupational cohorts, principally the Mayak worker cohort [40][41][42][44][45][46], the Semipalatinsk cohort [53], and, with a rather smaller number of people included, the French nuclear fuel cycle workers [47,48] and the Korean radiation worker cohorts [51,52].For the purposes of maximizing statistical power, specific CVD outcomes are analyzed together, but potential confounders could act differently on the different outcomes and their specific effect could be unseen in a pooled analysis of heterogeneous outcomes.
As summarized in Tables 1, 2 and 3, in general many epidemiological studies now have quite rich lifestyle, environmental and medical information.As highlighted in the Results there are over 30 other studies that clearly have such cofactor information, although in all cases best use has not been made of this in the publications for the purposes of assessing effects of potential confounding factors or effect modifying factors.
A limitation of our analysis is that statistical significance cannot be attached to the difference made by potentially confounding factors, since the reported coefficients would necessarily be highly correlated, and from the published data this correlation is impossible to determine.We therefore judge that this has to remain as we describe it in the Methods, based simply on the size and sign of the coefficients.However, as we outline in the Methods, one of the criteria for risk modifying factors is based on statistical significance.Clearly the particular levels of magnitude we chose to determine the seriousness of potentially confounding variables, likewise the levels of difference made by risk modifying factors are both somewhat arbitrary.Another limitation of the analysis is the degree of overlap in two particular studies, specifically two of the most important and informative ones, the LSS [9,10,37] and the Mayak workers [40][41][42][43][44][45][46], although not in any of the other studies listed in Tables 1, 2, 3 and 4.However, given the form of the analysis, bias would not result from this.At most there would be a tendency for findings (of potential confounding or risk modification) to be inflated by these correlated findings.As may be inferred from the results presented in Tables 3  and 4, there is little evidence for this, although the Mayak worker data do show a similar direction of effect made by adjustment to two overlapping mortality endpoints, IHD and all CVD [41].
In many studies in which adjustment is made for certain covariates, these are assayed at a number of time points and this information is then used to adjust for health endpoints after that point.Some of these covariates may also affect competing risks, for example cancer, and it is possible that they may affect both baseline CVD risk as well as radiation-associated excess risk; however, the evidence we have presented (Tables 1, 2 and 3) does not suggest that this is likely.Competing risks may well not be independent of CVD, so that the censoring they introduce will be informative.In this case consideration may have to be given to non-standard ways of analyzing the data.There are a number of statistical methods to assess effects of two or more competing risks [126].One of most popular is the so-called subdistribution hazard of Fine and Gray [127].
In summary, because of the multifactorial etiology of CVD, medical and lifestyle factors are clearly crucial variables to take into account in analysis of the dose response of these endpoints.The heterogeneity of the studied populations and of the type of exposure and dosimetry complicates drawing conclusions on the impact of medical and lifestyle factors on the dose response relationship between exposure to radiation and CVD.Nevertheless, we found a large number of studies in which there is information on effect of adjusting for certain lifestyle/environmental/medical variables, although in the larger number of studies previously assessed this information was not available, even if the relevant variables had clearly been assessed.We found limited evidence of potential confounding of radiation effects on CVD (Tables 1, 2 and 3); substantial differences were made by adjustment in four studies, but the uncertainties in all cases were substantial, so that little weight can be attached.There is much less information on potential modifying variables of radiation effect; nevertheless there is some evidence of the effects of age at exposure and sex, although not always in the same endpoints in different studies.However, in most of the studies reported here no analysis was reported of modifying effects of these or any other variable (Tables 1, 2 and 3).There is little evidence of modification resulting from variation in lagging period (Table 4).Efforts should be made to include in future studies as much as possible precise information on these variables and if available specific analysis on their impact on the dose response relationship should be assessed.It is important that analyses of radiation-associated CVD clearly demonstrate the effect of adjustment for the available lifestyle/environmental/medical variables, and also assess the potential modifying effect of these variables on the radiation dose response.

[ 9 ]
with smoking and drinking in the stratification b 90% CI c assuming a lag period of 10 years d adjusting for sex, SES e based on counter-matched analysis f prevalence excess odds ratio per Gy g estimate derived by fitting a linear model by (inverse-variance) weighted least squares, applied to the adjusted odds ratio (OR) provided in Table

Table 1
Unadjusted or adjusted estimated excess relative risk of cardiovascular diseases in various therapeutically and diagnostically treated groups, exposed at moderate or high radiation doses and high dose rates.All analyses adjust for age

Table 1 (continued) Study Reference Organ used Variables (other than age, sex, year) available to assess possible confounding Endpoint Mortality, incidence or prevalence Unadjusted (or less adjusted) excess relative risk Gy −1 (95% CI) Fully adjusted excess relative risk Gy
BC breast cancer, BMI body mass index, CAC coronary artery calcium, CAD coronary artery disease, CI confidence interval, COPD chronic obstructive pulmonary disease, CTCAE v Common Terminology Criteria for Adverse Events version, CVD cardiovascular disease, ERR excess relative risk, HER2 human epidermal growth factor receptor 2, HL Hodgkin lymphoma; HRT hormone replacement therapy, ICD International Classification of Diseases, IHD ischemic heart disease, KPS Karnovsky performance score, LAD left anterior descending artery, MHD mean heart dose, MLVD mean left ventricular dose, NA not available, RR relative risk, SES socioeconomic status, WHO/ISH World Health Organization/International Society of Hypertension * ratio of adjusted to unadjusted estimates outside the interval [0.667, 1.5] ** ratio of adjusted to unadjusted estimates outside the interval [0.5, 2]

Table 2
Unadjusted or adjusted estimated excess relative risks of cardiovascular diseases in the Japanese atomic bomb survivors and in other groups with moderate-or lowdose radiation exposure, with mean dose generally < 0.5 Gy

Table 2
(continued) (10)body mass index, CI confidence interval, CRP C-reactive protein, CeVD cerebrovascular disease, CVD cardiovascular disease, ICD International Classification of Diseases, ERR excess relative risk, GFR glomerular filtration rate, HDL high density lipoprotein, H p(10)personal dose equivalent at 10 mm depth, LDL low density lipoprotein, OR odds ratio, SES socioeconomic status * ratio of adjusted to unadjusted estimates outside the interval [0.667, 1.5] ** ratio of adjusted to unadjusted estimates outside the interval [0.5, 2] *** ratio of adjusted to unadjusted estimates negative † ratio of estimate with interaction modifier to that without modifier outside the interval [0.667, 1.5] † † ratio of estimate with interaction modifier to that without modifier outside the interval [0.5, 2] † † † ratio of estimate with interaction modifier to that without modifier negative a analysis derived from Table 3 of Yamada et al. [9] with smoking and drinking in the stratification b assuming a lag period of 5 years c assuming a lag period of 10 years d assuming a lag period of 15 years e dropping smoking and alcohol consumption from adjustment f adjusting for sex, SES g based on counter-matched analysis h adjusting for age at exit i adjusting for age at exit + entrance, medical diagnostic dose j prevalence excess odds ratio per Gy k estimate derived by fitting a linear model by (inverse-variance) weighted least squares, applied to the adjusted odds ratio (OR) provided in

Table 3
Unadjusted or adjusted estimated excess relative risk of cardiovascular diseases in various groups in which there is pronounced effect of adjustment for potential confounder variables, or significant variation by modifying factors.All analyses adjust for age

Table 4
Variation with latency of estimated excess relative risks of cardiovascular diseases in occupationally and environmentally exposed groups

Table 5
Effect of modifying variables on absolute risk in radiobiological animal data